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Found 7 papers, 1 papers with code
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition
We compare biases found in pre-trained models to biases in downstream models adapted to the task of Speech Emotion Recognition (SER) and find that in 66 of the 96 tests performed (69%), the group that is more associated with positive valence as indicated by the SpEAT also tends to be predicted as speaking with higher valence by the downstream model.
Extending Explainable Boosting Machines to Scientific Image Data
As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern.
The 2022 NIST Language Recognition Evaluation
In 2022, the U. S. National Institute of Standards and Technology (NIST) conducted the latest Language Recognition Evaluation (LRE) in an ongoing series administered by NIST since 1996 to foster research in language recognition and to measure state-of-the-art technology.
The 2021 NIST Speaker Recognition Evaluation
Evaluation results indicate: audio-visual fusion produce substantial gains in performance over audio-only or visual-only systems; top performing speaker and face recognition systems exhibited comparable performance under the matched domain conditions present in this evaluation; and, the use of complex neural network architectures (e. g., ResNet) along with angular losses with margin, data augmentation, as well as long duration fine-tuning contributed to notable performance improvements for the audio-only speaker recognition task.
The NIST CTS Speaker Recognition Challenge
The US National Institute of Standards and Technology (NIST) has been conducting a second iteration of the CTS challenge since August 2020.
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data.
Compact Representation of Uncertainty in Clustering
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its corresponding trellis for exact probability distributions in Markov models).
